Recent work in decentralized, schedule-driven traffic control has demonstrated the ability to improve the efficiency of traffic flow in complex urban road networks. In this approach, a scheduling agent is associated with each intersection. Each agent senses the traffic approaching its intersection and in real-time constructs a schedule that minimizes the cumulative wait time of vehicles approaching the intersection over the current look-ahead horizon. In order to achieve network level coordination in a scalable manner, scheduling agents communicate only with their direct neighbors. Each time an agent generates a new intersection schedule it communicates its expected outflows to its downstream neighbors as a prediction of future demand and these outflows are appended to the downstream agent’s locally perceived demand. In this paper, we extend this basic coordination algorithm to additionally incorporate the complementary flow of information reflective of an intersection’s current congestion level to its upstream neighbors. We present an asynchronous decentralized algorithm for updating intersection schedules and congestion level estimates based on these bi-directional information flows. By relating this algorithm to the self-optimized decision making of the basic operation, we are able to approach network-wide optimality and reduce inefficiency due to strictly self-interested intersection control decisions.